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Loss Functions

1162 directly classified papers

Papers per year

Papers

DA-IICT/IIITV System for Low Resource Speech Recognition Challenge 2018 INTERSPEECH 2018
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates NIPS 2018
A loss framework for calibrated anomaly detection NIPS 2018
Connectionist Temporal Classification with Maximum Entropy Regularization NIPS 2018
Improving Pairwise Ranking for Multi-Label Image Classification CVPR 2017
BranchOut: Regularization for Online Ensemble Tracking With Convolutional Neural Networks CVPR 2017
Loss Max-Pooling for Semantic Image Segmentation CVPR 2017
Learning with Average Top-k Loss NIPS 2017
Adaptive Semi-Supervised Learning with Discriminative Least Squares Regression IJCAI 2017
Don’t understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures ACL 2017
Probability Calibration Trees ACML 2017
Variance-based Regularization with Convex Objectives NIPS 2017
Confident Multiple Choice Learning ICML 2017
On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models NIPS 2017
Value-Aware Loss Function for Model-based Reinforcement Learning AISTATS 2017
Uncorrelation and Evenness: a New Diversity-Promoting Regularizer ICML 2017
Forest-type Regression with General Losses and Robust Forest ICML 2017
An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective EMNLP 2017
Importance-Aware Semantic Segmentation for Autonomous Driving System IJCAI 2017
Multi-Class Support Vector Machine via Maximizing Multi-Class Margins IJCAI 2017
Multi-Observation Elicitation COLT 2017
A Strongly Quasiconvex PAC-Bayesian Bound ALT 2017
Regularising Non-linear Models Using Feature Side-information ICML 2017
On Calibration of Modern Neural Networks ICML 2017
Improving the Generalization Performance of Multi-class SVM via Angular Regularization IJCAI 2017